Senior Infrastructure Engineer
Build and operate multi-tenant, distributed infrastructure for an enterprise AI platform across Kubernetes and multiple clouds. The role requires 5+ years of platform, infrastructure, or backend engineering experience, strong Golang and Python skills, and deep expertise in reliability, databases, and distributed systems.
About the job
Responsibilities
- Design, own, and evolve scalable microservices architectures on Kubernetes across GCP, Azure, and AWS, including multi-tenant isolation with namespaces, network policies, per-tenant resource quotas, and RBAC.
- Build core platform and data-plane components in Golang and Python, including data ingestion, knowledge-base indexing, vector and graph search, application connectivity, workflow automation, and ML operations, against explicit latency and throughput SLOs.
- Own service-to-service communication, including gRPC/Protobuf API contracts, service meshes such as Istio and Linkerd, load balancing, retries, timeouts, and circuit breaking.
- Make and document architectural tradeoffs involving partitioning and sharding, consistency models, caching tiers, and build-versus-buy decisions.
- Define reliability contracts including SLIs, SLOs, error budgets, capacity planning, autoscaling, and graceful degradation.
- Design and operate observability using Prometheus, Grafana, OpenTelemetry, distributed tracing, and real-time alerting.
- Drive DevOps and platform-engineering practices using infrastructure as code, Helm, GitOps, and CI/CD pipelines.
- Optimize performance and cost through profiling, load testing, latency budgets, and cost-per-request analysis.
- Participate in on-call rotations and lead incident response and root-cause analysis.
Requirements
- Bachelor's degree in Computer Science or a related field.
- 5+ years of experience in platform, infrastructure, or backend engineering.
- Strong computer science fundamentals, including data structures, algorithms, operating systems, and networking.
- Proficiency in Golang and Python.
- Production experience with Docker, Kubernetes, and microservices architecture.
- Hands-on experience with GCP, Azure, or AWS; multi-cloud experience is a strong plus.
- Strong database expertise, including query and read/write-path optimization, partitioning and sharding, replication, consistency models, NoSQL, graph stores, the CAP theorem, and database internals.
- Distributed-systems experience with idempotency, backpressure, delivery semantics, and message queues such as Kafka, Pulsar, NATS, or Pub/Sub.
- Track record of building platforms from the ground up for use by other engineering teams.
Nice-to-haves
- Experience operating systems at high scale, including high QPS and large data volumes.
- Experience with secrets management, mTLS, RBAC, OIDC/SAML, and network policy.
- Experience with vector databases such as pgvector, Pinecone, or Milvus, and graph databases such as Neo4j or Neptune.
- Open-source contributions to infrastructure projects, such as Kubernetes operators.
Compensation and Benefits
- Compensation is determined by location, level, job-related knowledge, skills, and experience.
- Certain roles may be eligible for variable compensation, equity, and benefits.
Skills
Kubernetes, Go, Python, Docker, GCP, Azure, AWS, Microservices, gRPC, Istio, Linkerd, Terraform, Helm, Argo CD, Prometheus
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